Big data is key to success in any high-level industry, including fashion. As stated in a recent study, big data is increasingly being used in the fashion industry for “trend forecasting, supply chain management, customer behavior analysis, preferences, and emotions.” Last year, Business of Fashion (BoF) estimated that over 75% of fashion retailers would invest in AI, which makes sense considering that in 2020, close to $1 trillion is expected to be spent on e-commerce purchases by consumers.
Personalization Drives a Greater Need for Big Data
Fashion is an industry that is highly sensitive to changing demands and it is also one that is moving towards greater personalization. Buyers wish to be presented with various options that cater to their style choices, contexts (e.g. work, social clothing), and cultural and musical influences. As the demand for personalization increases, so do does ‘mass customization’ of garments, in a bid to prevent the buildup of unwanted stock.
Big Data to Match Consumer Expectations
Manufacturers are currently using online data (obtained from sales, market research, and social media feedback and purchasing analytics) to obtain data on specific categories. These include fabric choice, which is intricately tied into emotions, textural and structural preferences, and seasons. Another key category reliant on big data is design, which in turn is influenced by human emotion, context, cultural influences, themes, and the like. Also key for fashion houses is data on the body of the purchasers, achieve in 2D or 3D form. For 2D, measurements or sizes of items purchased or searched for are sufficient. 3D takes it a step further, relying on body scanners to better understand both size and body type. Color (warm vs cool, soft or hard, pastel or bold and the like), and technical design (required means of sewing, weaving, knitting, etc) choices are also reliant on data obtained.
Driving Key Decisions
Information gleaned via AI and other sources enable fashion houses to make key decisions related to both online and retail shopping. For instance, companies like Glossier and Warby Parker, both of whom began as online retailers, used data obtained from clients to determine the most fitting locations for their physical stores. Clients are encouraged to sign up for discounts and key information, in return for answering surveys about the items they prefer. Savvy companies are additionally offering ‘personal shopper’ services to heighten customization.
Predictions for Product Demands
For some fashion houses, predicting the types of items with big sales potential is relatively easy. This is the case, for instance, for brands specializing in items like festival wear for big events across the globe — think Coachella, Glastonbury, or the New Orleans Jazz Festival. Prediction in this area is straightforward because they are seasonal, and theme-inspired. Essential festival ware has a wow factor that is one of a kind. Often, items worn are inspired on the 1960s and 1970s. Crocheted tops, high boots, large sunglasses, fringed bags and the like never fail to inspire. However, for other more fluid or ad hoc events, prediction can be far trickier. Thus, Rue La La and MIT recently teamed up to improve AI predictions, resulting in a 10% revenue increase.
Big Data and Influencers
Predictive technology will be doing more than identify key colors, cuts, and styles. It will also help brands identify influencers whom they can ally to prior to the latter’s ‘boom.’ Finding influencers allies is key in the current fashion industry, since social media itself has become omnipresent. As stated by researchers as far back as 2018, “The average earned media value per $1 spend on Influencer Marketing was $5,20. This means the ROI was more than 5 times.” She also reminds us that micro influencers — whose spend is considerably less for fashion houses than macro influencers — are playing an increasingly important role in marketing, since they have strong relationships and a high trust factor with their audiences.
Big data has completely changed the fashion game. Currently, fashion houses are using data to do everything from customize clothing to upgrade clothing performance, select fabrics, and change their methods of merchandizing. Big data is also providing a key tool in predicting trends and in indicating the right influencers to back.